Data Version Control (DVC) is a system designed to manage and track changes in datasets, similar to how Git manages code. In production-level environments, where datasets are continuously updated and modified by multiple users, maintaining a single source of truth is crucial. DVC provides an audit trail to keep track of all changes made to datasets, ensuring that users can access the most current version while also being able to reference previous versions when needed ¹.

DVC operates alongside Git, mimicking its functionality for large files and serving as a metadata management tool. Instead of directly handling large datasets, DVC generates lightweight metadata file placeholders, allowing for efficient tracking of these assets without overwhelming Git’s capabilities ⁵. This approach helps data scientists and engineers manage their workflows more effectively, especially when dealing with complex data projects.

In summary, DVC is essential for maintaining version control in data science projects, ensuring that users can collaborate seamlessly while keeping track of changes over time.